4. Gender in the Impact Assessment of the European Commission
Bibliographic record
Abstract
Gender in the Impact Assessment of the European CommissionThis second empirical chapter, presents interview results derived from the European Commission's practices.The first part explains the context of the EU's political, multilevel governance and administrative system and provides a chronological reiteration of the development of the EU's ex-ante IA system and the position of GIA within it.The second section presents a critique of the guidelines currently available in the Commission's IA system.The third part explores the role of the EU's gender equality architecture with regard to gender impact assessment.In the fourth and main part, I present the interview evaluation and the stance the European experts have taken, contextualised with the document analysis of tools and supporting literature, as presented in the subchapters before.As in the previous chapter on Canada, part five attempts a summary of the main findings on the position of gender equality in the EU's IA system.Again, in the EU context, impact assessment (IA) is used as innate terminology, referring to ex-ante policy and programme IA (unless otherwise indicated). 11 | For a detailed discussion on international IA terminology, see chapter 1.3.; for tool typologies see subchapter 1.6.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".